Analysis of Moving Human Micro-Doppler Signature in Forest Environments

Garcia-Rubia, Jose Miguel; Kıłıc, Özlem; Dang, Vinh Quang; Nguyen, Quang Minh; Tran, Nghia

doi:10.2528/pier14012306

Cited by 36 publications

(25 citation statements)

References 37 publications

(41 reference statements)

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“…Therefore, non-wearable sensors such as radar can be considered as a more efficient alternative system as it can overcome many limitations that other vision-based systems suffer from. For example, radar can be used under poor lighting and harsh environmental conditions as dust and smoke [7]. Moreover, the micro-Doppler (µ-D) signature presented in [8] has introduced the feasibility of using radar for studying the human gait.…”

Section: Introductionmentioning

confidence: 99%

Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers

Abdulatif

Aziz

Armanious

et al. 2019

2019 IEEE Radar Conference (RadarConf)

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Obtaining a smart surveillance requires a sensing system that can capture accurate and detailed information for the human walking style. The radar micro-Doppler (µ-D) analysis is proved to be a reliable metric for studying human locomotions. Thus, µ-D signatures can be used to identify humans based on their walking styles. Additionally, the signatures contain information about the radar cross section (RCS) of the moving subject. This paper investigates the effect of human body characteristics on human identification based on their µ-D signatures. In our proposed experimental setup, a treadmill is used to collect µ-D signatures of 22 subjects with different genders and body characteristics. Convolutional autoencoders (CAE) are then used to extract the latent space representation from the µ-D signatures. It is then interpreted in two dimensions using t-distributed stochastic neighbor embedding (t-SNE). Our study shows that the body mass index (BMI) has a correlation with the µ-D signature of the walking subject. A 50-layer deep residual network is then trained to identify the walking subject based on the µ-D signature. We achieve an accuracy of 98% on the test set with high signal-to-noise-ratio (SNR) and 84% in case of different SNR levels.

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Section: Introductionmentioning

confidence: 99%

Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers

Abdulatif

Aziz

Armanious

et al. 2019

2019 IEEE Radar Conference (RadarConf)

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“…The human path is a direct path toward the radar starting the origin. A brief description on the details of system parameters for the radar range for this scenario is provided in [Garcia-Rubia et al, 2014].…”

Section: Simulation Resultsmentioning

confidence: 99%

Detection of moving human micro‐Doppler signature in forest environments with swaying tree components by wind

et al. 2015

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The objective of this paper is to investigate human motion in forest medium with swaying tree components due to time-varying wind effects and to observe the characteristics of the received Doppler signature from the scene. We provide the results of an accurate model accounting for the key contributions to the Doppler signature in this scenario. A realistic walking motion is generated using an analytical model extracted from empirical data. The swaying canopy motion is modeled by employing a spring response mechanism to the wind force. The backscattered field calculations from the scene comprise of contributions from the forest (including trunks, branches, and the ground) and human, and the interactions between them. An analytical forest scattering model, which accounts for the ground effects, is used to calculate the contribution from the forest. The attenuation effects due to the vegetation are accounted for. In order to characterize the effects of human motion accurately, a full wave technique, namely, method of moments (MOM) enhanced with fast multipole method (FMM), is employed for the human scattering calculations. A parallel version of MOM-FMM is implemented on a graphics processing unit based cluster to handle the large problem size. The human walking signatures created by the model are analyzed for different winds.

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“…In recent years, there has been a great research interest in human activity classification using micro-Doppler signatures [10], [11], [12], [13]. In [14], a low-power pulse-Doppler radar that operates at 5.8 GHz was used to collect the micro-Doppler signatures of three different activities (walking, running, and crawling) performed by four subjects on a treadmill.…”

Section: Introductionmentioning

confidence: 99%

Human Activity Detection and Coarse Localization Outdoors Using Micro-Doppler Signatures

2019

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Human activity detection outdoors is emerging as a very important research field due to its potential application in surveillance, assisted living, search and rescue, and military applications. For such applications it is important to have detailed information about the human target, for example, whether the detected target is a single person or a group of people, what activity a target is performing, and the rough location of the target. In this paper, we propose novel usage of machine learning techniques to perform subject classification, human activity classification, people counting, and coarse localization by classifying micro-Doppler signatures obtained from a low-cost and low-power radar system. Our experiments were performed outdoors. For feature extraction of micro-Doppler signatures, we applied a two-directional two-dimensional principle component analysis (2D2PCA). Our results show that by applying 2D2PCA, the accuracy results of Support Vector Machine (SVM) and knearest neighbors (kNN) classifiers were greatly improved. We also designed and implemented a Convolutional Neural Network (CNN) for the target classifications in terms of type, number, activity and coarse localization. Our CNN model obtained very high classification accuracies (97% to 100%), which are superior to the best results obtained by SVM and kNN. Finally, we investigated the effects of the frame length of the sliding window, the angle of the direction of movement, and the number of radars used on the classification performance, providing valuable guidelines for machine learning modeling and experimental setup of micro-Doppler based research and applications.

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Analysis of Moving Human Micro-Doppler Signature in Forest Environments

Cited by 36 publications

References 37 publications

Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers

Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers

Detection of moving human micro‐Doppler signature in forest environments with swaying tree components by wind

Human Activity Detection and Coarse Localization Outdoors Using Micro-Doppler Signatures

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